User menu

Accès à distance ? S'identifier sur le proxy UCLouvain

Model selection with cross-validations and bootstraps - Application to time series prediction with RBFN models

Primary tabs

Lendasse, Amaury [UCL] Wertz, Vincent [UCL] Verleysen, Michel [UCL]

This paper compares several model selection methods, based on experimental estimates of their generalization errors. Experiments in the context of nonlinear time series prediction by Radial-Basis Function Networks show the superiority of the bootstrap methodology over classical cross-validations and leave-one-out.

Document type Communication à un colloque (Conference Paper) – Présentation orale avec comité de sélection
Access type Accès restreint
Publication date 2003
Language Anglais
Conference "Joint International Conference on Artificial Neural Networks (ICANN)/International on Neural Information Processing (ICANN 2003)", ISTANBUL (Turkey) (du 26/06/2003 au 29/06/2003)
Journal information "Lecture Notes in Computer Science" - Vol. 2714, p. 573-580 (2003)
Peer reviewed yes
issn 0302-9743
Host document
Publisher Springer-Verlag (Berlin-Heidelberg)
Affiliations UCL - FSA/INMA - Département d'ingénierie mathématique
UCL - FSA/ELEC - Département d'électricité
Links
Bibliographic reference Lendasse, Amaury ; Wertz, Vincent ; Verleysen, Michel. Model selection with cross-validations and bootstraps - Application to time series prediction with RBFN models.Joint International Conference on Artificial Neural Networks (ICANN)/International on Neural Information Processing (ICANN 2003) (ISTANBUL (Turkey), du 26/06/2003 au 29/06/2003). In: Lecture Notes in Computer Science, Vol. 2714, p. 573-580 (2003)
Permanent URL http://hdl.handle.net/2078.1/61492